National and Subnational estimates for Russia

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Russia. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods or our paper for further explanation).

Table of Contents


Using data available up to the: 2020-06-20

Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.

Expected daily confirmed cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated confirmed cases with a date of infection on the 2020-05-12) in Russia, stratified by region, can be summarised by whether confirmed cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 confirmed cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-05-12)

Table 1: Latest estimates (as of the 2020-05-12) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time (when negative this corresponds to the halving time), and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New confirmed cases by infection date 8808 (8323 – 9288)
Expected change in daily cases Increasing
Effective reproduction no. 1 (1 – 1)
Doubling/halving time (days) 120 (66 – 920)
Adjusted R-squared 0.61 (0.26 – 0.98)

Confirmed cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates from existing data are shown up to the 2020-05-12 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Time-varying rate of growth and doubling time


Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (when negative this corresponds to the halving time), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates from existing data are shown up to the 2020-05-12. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and confirmed case count estimates by date of infection


Figure 4: Confirmed cases with date of infection on the 2020-05-12 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmedcases. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most new confirmed cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-05-12 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases


Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-05-12 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-05-12 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in all regions

Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-05-12 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Latest estimates (as of the 2020-05-12)

Table 2: Latest estimates (as of the 2020-05-12) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown.
Region New confirmed cases by infection date Expected change in daily cases Effective reproduction no. Doubling/halving time (days)
Adygea Republic 42 (28 – 53) Increasing 1.4 (1 – 1.7) 8.3 (4.6 – 53)
Altai Krai 50 (37 – 62) Likely increasing 1.2 (1 – 1.4) 16 (6.8 – -49)
Arkhangelsk Oblast 44 (32 – 57) Likely increasing 1.1 (0.9 – 1.4) 22 (7.5 – -24)
Astrakhan Oblast 46 (32 – 58) Unsure 1.1 (0.8 – 1.3) 95 (11 – -14)
Bashkortostan Republic 92 (74 – 108) Unsure 1.1 (0.9 – 1.2) 43 (12 – -28)
Belgorod Oblast 73 (59 – 88) Unsure 1.1 (0.9 – 1.2) 62 (12 – -21)
Bryansk Oblast 107 (88 – 126) Likely increasing 1.1 (1 – 1.3) 19 (9.2 – -170)
Buryatia Republic 43 (30 – 54) Unsure 1 (0.8 – 1.2) 170 (11 – -13)
Chechen Republic 25 (14 – 33) Unsure 1 (0.7 – 1.2) -64 (10 – -7.8)
Chelyabinsk Oblast 85 (66 – 100) Likely increasing 1.1 (0.9 – 1.2) 43 (12 – -27)
Chuvashia Republic 84 (68 – 100) Unsure 1.1 (0.9 – 1.2) 51 (12 – -24)
Dagestan Republic 96 (79 – 112) Unsure 1 (0.8 – 1.1) -90 (23 – -15)
Ingushetia Republic 43 (30 – 54) Unsure 1 (0.8 – 1.2) 490 (11 – -12)
Irkutsk Oblast 60 (46 – 74) Increasing 1.3 (1 – 1.5) 10 (5.6 – 61)
Ivanovo Oblast 53 (39 – 67) Unsure 1 (0.8 – 1.1) -31 (23 – -9.5)
Kabardino-Balkarian Republic 69 (53 – 82) Unsure 1 (0.8 – 1.1) -71 (19 – -12)
Kaliningrad Oblast 36 (23 – 46) Unsure 1 (0.8 – 1.3) 98 (9.7 – -12)
Kalmykia Republic 30 (18 – 38) Unsure 1 (0.8 – 1.3) 140 (9.3 – -11)
Kaluga Oblast 113 (94 – 131) Likely increasing 1.1 (1 – 1.2) 35 (12 – -41)
Kamchatka Krai 21 (12 – 30) Likely increasing 1.2 (0.8 – 1.6) 13 (4.8 – -17)
Karachay-Cherkess Republic 17 (9 – 25) Unsure 1 (0.7 – 1.4) -360 (7.6 – -7.2)
Khabarovsk Krai 57 (42 – 70) Likely increasing 1.1 (0.9 – 1.3) 27 (8.8 – -25)
Khakassia Republic 29 (19 – 38) Unsure 1.1 (0.8 – 1.3) 92 (8.7 – -11)
Khanty-Mansi Autonomous Okrug 75 (56 – 88) Likely increasing 1.1 (0.9 – 1.3) 27 (9.8 – -35)
Kirov Oblast 33 (22 – 43) Unsure 1.1 (0.8 – 1.3) 53 (8.3 – -12)
Komi Republic 27 (16 – 36) Likely increasing 1.2 (0.9 – 1.5) 13 (5.1 – -26)
Krasnodar Krai 94 (75 – 110) Unsure 1 (0.9 – 1.1) -210 (19 – -17)
Krasnoyarsk Krai 202 (170 – 232) Increasing 1.4 (1.2 – 1.5) 8.6 (6.1 – 15)
Kursk Oblast 82 (64 – 98) Unsure 1.1 (0.9 – 1.2) 56 (13 – -23)
Leningrad Oblast 71 (54 – 86) Unsure 1 (0.8 – 1.2) 37000 (15 – -15)
Lipetsk Oblast 62 (45 – 76) Likely increasing 1.1 (0.9 – 1.3) 28 (9.1 – -26)
Magadan Oblast 9 (2 – 15) Likely increasing 1.4 (0.7 – 2) 7.9 (2.8 – -9.1)
Mari El Republic 46 (32 – 59) Unsure 1.1 (0.9 – 1.3) 24 (7.7 – -22)
Mordovia Republic 34 (22 – 44) Unsure 1 (0.7 – 1.2) -76 (12 – -9)
Moscow 3570 (3390 – 3777) Decreasing 0.9 (0.8 – 0.9) -18 (-23 – -14)
Moscow Oblast 947 (865 – 1028) Unsure 1 (1 – 1.1) -420 (60 – -48)
Murmansk Oblast 41 (28 – 52) Unsure 1 (0.7 – 1.2) 45 (9.2 – -15)
Nizhny Novgorod Oblast 246 (217 – 272) Likely decreasing 1 (0.9 – 1) -43 (90 – -18)
North Ossetia - Alania Republic 75 (56 – 88) Unsure 1 (0.8 – 1.2) -3300 (16 – -16)
Novosibirsk Oblast 76 (58 – 91) Unsure 1 (0.9 – 1.2) 83 (13 – -19)
Omsk Oblast 59 (42 – 71) Increasing 1.3 (1 – 1.5) 11 (6 – 100)
Orel Oblast 53 (38 – 67) Unsure 1 (0.8 – 1.1) -71 (16 – -11)
Orenburg Oblast 43 (29 – 54) Unsure 1 (0.8 – 1.2) -49 (16 – -9.7)
Penza Oblast 77 (61 – 91) Increasing 1.2 (1 – 1.4) 13 (6.8 – 98)
Perm Krai 52 (37 – 65) Likely increasing 1.2 (0.9 – 1.4) 14 (6.4 – -74)
Primorsky Krai 61 (48 – 77) Unsure 1.1 (0.9 – 1.2) 46 (11 – -19)
Rostov Oblast 123 (101 – 141) Likely increasing 1.1 (1 – 1.3) 21 (10 – -180)
Ryazan Oblast 94 (76 – 110) Unsure 1 (0.9 – 1.2) 640 (17 – -18)
Saint Petersburg 472 (422 – 513) Increasing 1.1 (1 – 1.2) 33 (18 – 210)
Sakha (Yakutiya) Republic 50 (37 – 63) Unsure 1.1 (0.9 – 1.3) 43 (9.7 – -18)
Samara Oblast 75 (60 – 91) Unsure 1 (0.9 – 1.2) 130 (14 – -17)
Saratov Oblast 100 (78 – 116) Unsure 1.1 (0.9 – 1.2) 43 (12 – -28)
Sevastopol 6 (0 – 11) Unsure 1.4 (0.5 – 2.3) 8 (2.1 – -4.8)
Smolensk Oblast 56 (41 – 69) Unsure 1 (0.8 – 1.2) -44 (19 – -10)
Stavropol Krai 68 (51 – 82) Likely increasing 1.1 (0.9 – 1.3) 27 (9.4 – -30)
Sverdlovsk Oblast 139 (115 – 159) Unsure 1.1 (0.9 – 1.2) 88 (17 – -28)
Tambov Oblast 85 (67 – 101) Unsure 1 (0.9 – 1.2) -9300 (17 – -17)
Tatarstan Republic 81 (64 – 96) Unsure 1 (0.9 – 1.1) -420 (17 – -16)
Tomsk Oblast 19 (10 – 27) Unsure 1 (0.7 – 1.3) -46 (8.7 – -6.4)
Tula Oblast 94 (75 – 111) Unsure 1 (0.9 – 1.1) 260 (16 – -19)
Tver Oblast 35 (23 – 45) Unsure 1.1 (0.8 – 1.3) 44 (8.6 – -13)
Tyumen Oblast 41 (28 – 51) Unsure 1 (0.8 – 1.1) -34 (19 – -8.9)
Tyva Republic 47 (34 – 58) Increasing 1.3 (1 – 1.6) 10 (5.3 – 150)
Udmurt Republic 12 (4 – 17) Unsure 1.1 (0.6 – 1.5) 27 (4.6 – -7.1)
Ulyanovsk Oblast 83 (64 – 99) Likely increasing 1.1 (1 – 1.3) 21 (8.8 – -57)
Vladimir Oblast 77 (58 – 91) Unsure 1.1 (0.9 – 1.2) 53 (12 – -23)
Volgograd Oblast 82 (66 – 98) Unsure 1 (0.9 – 1.2) -100 (20 – -15)
Voronezh Oblast 44 (31 – 54) Unsure 1 (0.8 – 1.2) 410 (12 – -12)
Yamalo-Nenets Autonomous Okrug 67 (51 – 81) Unsure 1 (0.8 – 1.1) -620 (15 – -15)
Yaroslavl Oblast 111 (92 – 130) Likely increasing 1.1 (1 – 1.3) 20 (9.5 – -180)
Zabaykalsky Krai 35 (24 – 45) Unsure 1.1 (0.9 – 1.4) 28 (7.5 – -17)

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Mironov, Sergey. 2020. “COVID-19 Data from Jhu Csse, Updated with Details on Russian Regions.” Github Repository. https://github.com/grwlf/COVID-19_plus_Russia.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.